Fill parameters for inhalation powders
Prediction based on correlation algorithms
Prozesstechnik
Abstract
This article presents the development and initial validation of a correlation-based prediction tool for defining filling parameters for micro-dosed inhalation powders processed by membrane filling technology. Powder properties such as bulk density, compressibility, and particle size distribution were linked to critical process parameters including nozzle diameter, cavity depth, filling offset, filling time, and vacuum. Different regression models were evaluated using an automated Python script to identify the most suitable correlation for each parameter relationship. Validation with 7 independent production batches showed that 5 batches met the target range for mean fill weight, while all batches fulfilled the Relative Standard Deviation (RSD) criterion of ≤3.0 %. The tool therefore supports early process development by enabling the preselection of suitable parameter ranges, reducing experimental effort, material consumption, and handling time.
Correspondence:
Dr. Elke Sternberger-Rützel
Harro Höfliger Verpackungsmaschinen GmbH,
Helmholtzstr. 4, 71573 Allmersbach i.T.
elke.sternberger-ruetzel@hoefliger.com
 | Dr. Elke Sternberger-Rützel is a pharmacist with a PhD in Pharmaceutical Technology and over 20 years of experience in the pharmaceutical and healthcare industry. Her expertise spans pharmaceutical development, analytical sciences, quality management, and microdosing technologies. She has held leadership positions at Grünenthal, Bosch Packaging Technology, and Catalent, driving innovation and operational excellence |
Schließen Sie hier ein Abonnement ab und profitieren Sie von den vielseitigen Nutzungsmöglichkeiten.
